Development and Testing of a GPS-Augmented Multi-Sensor Vehicle Navigation System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
An integrated multi-sensor vehicle navigation system is presented that uses a low-cost rate gyro and differential odometry to supplement GPS under signal masking conditions such as tree foliage and urban canyons. Signal masking is often accompanied by extreme multi-path in urban centres with tall buildings, and is also found in wooded areas, enclosed car parks, tunnels, etc. The purpose of the system tested is to provide an accuracy of better than 20 metres almost 100% of the time throughout these interruptions, which are assumed to last up to a few minutes. The equipment used is discussed in detail, as is the method used for filtering measurements. Results are presented from tests carried out in an urban core with relatively long periods of signal loss – up to several minutes over a 6-km test circuit. Tests in urban canyons demonstrate that it is difficult to reach the above specifications with aiding from differential odometry alone due to the high precision of the wheel-scale factor required. However, with the use of a rate gyro and odometry, RMS errors are below 20 metres while availability is nearly 100%. Some of the large deviations could probably be better controlled if GPS multi-path errors were detected before they are allowed to corrupt the filtered solution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it